{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Follow up notebook that describes how accurately prophet predicted several weeks of website traffic.\n",
    "Full article is [here](http://pbpython.com/prophet-accuracy.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proj = pd.read_excel('https://github.com/chris1610/pbpython/blob/master/data/March-2017-forecast-article.xlsx?raw=True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the prediction (aka yhat) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-25</td>\n",
       "      <td>3.294797</td>\n",
       "      <td>2.770241</td>\n",
       "      <td>3.856544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-26</td>\n",
       "      <td>3.129766</td>\n",
       "      <td>2.564662</td>\n",
       "      <td>3.677923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-27</td>\n",
       "      <td>3.152004</td>\n",
       "      <td>2.577474</td>\n",
       "      <td>3.670529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-28</td>\n",
       "      <td>3.659615</td>\n",
       "      <td>3.112663</td>\n",
       "      <td>4.191708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-29</td>\n",
       "      <td>3.823493</td>\n",
       "      <td>3.279714</td>\n",
       "      <td>4.376206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      yhat  yhat_lower  yhat_upper\n",
       "0 2014-09-25  3.294797    2.770241    3.856544\n",
       "1 2014-09-26  3.129766    2.564662    3.677923\n",
       "2 2014-09-27  3.152004    2.577474    3.670529\n",
       "3 2014-09-28  3.659615    3.112663    4.191708\n",
       "4 2014-09-29  3.823493    3.279714    4.376206"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proj[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the values from log format and filter for the right daterange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proj[\"Projected_Sessions\"] = np.exp(proj.yhat).round()\n",
    "proj[\"Projected_Sessions_lower\"] = np.exp(proj.yhat_lower).round()\n",
    "proj[\"Projected_Sessions_upper\"] = np.exp(proj.yhat_upper).round()\n",
    "\n",
    "final_proj = proj[(proj.ds > \"3-5-2017\") & \n",
    "                  (proj.ds < \"5-20-2017\")][[\"ds\", \"Projected_Sessions_lower\", \n",
    "                                            \"Projected_Sessions\", \"Projected_Sessions_upper\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the Google Analytics file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "actual = pd.read_excel('https://github.com/chris1610/pbpython/blob/master/data/Traffic_20170306-20170519.xlsx?raw=True')\n",
    "actual.columns = [\"ds\", \"Actual_Sessions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>Actual_Sessions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-06</td>\n",
       "      <td>2227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-07</td>\n",
       "      <td>2093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-08</td>\n",
       "      <td>2068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-09</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-10</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds  Actual_Sessions\n",
       "0 2017-03-06             2227\n",
       "1 2017-03-07             2093\n",
       "2 2017-03-08             2068\n",
       "3 2017-03-09             2400\n",
       "4 2017-03-10             1888"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the predictions and the merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>Actual_Sessions</th>\n",
       "      <th>Projected_Sessions_lower</th>\n",
       "      <th>Projected_Sessions</th>\n",
       "      <th>Projected_Sessions_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-06</td>\n",
       "      <td>2227</td>\n",
       "      <td>1427.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>4289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-07</td>\n",
       "      <td>2093</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>3194.0</td>\n",
       "      <td>5458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-08</td>\n",
       "      <td>2068</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>3273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-09</td>\n",
       "      <td>2400</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>3172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-10</td>\n",
       "      <td>1888</td>\n",
       "      <td>958.0</td>\n",
       "      <td>1642.0</td>\n",
       "      <td>2836.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds  Actual_Sessions  Projected_Sessions_lower  Projected_Sessions  \\\n",
       "0 2017-03-06             2227                    1427.0              2503.0   \n",
       "1 2017-03-07             2093                    1791.0              3194.0   \n",
       "2 2017-03-08             2068                    1162.0              1928.0   \n",
       "3 2017-03-09             2400                    1118.0              1886.0   \n",
       "4 2017-03-10             1888                     958.0              1642.0   \n",
       "\n",
       "   Projected_Sessions_upper  \n",
       "0                    4289.0  \n",
       "1                    5458.0  \n",
       "2                    3273.0  \n",
       "3                    3172.0  \n",
       "4                    2836.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(actual, final_proj)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See how big the delta is between the prediction and actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      75.000000\n",
       "mean      739.440000\n",
       "std       711.001829\n",
       "min     -1101.000000\n",
       "25%       377.500000\n",
       "50%       619.000000\n",
       "75%       927.000000\n",
       "max      4584.000000\n",
       "Name: Session_Delta, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Session_Delta\"] = df.Actual_Sessions - df.Projected_Sessions\n",
    "df.Session_Delta.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Need to convert to just a date in order to keep plot from throwing errors\n",
    "df['ds'] = df['ds'].dt.date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick Plot of the delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IlgBGBCgJYIQQIli5ZmDqSeIFiJbtBETgkgBGCCGClWsGpp4yamQ7ARHYJIARQohg1dgM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\nQLoNva6sVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41b6edbc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "df.plot(\"ds\", \"Session_Delta\", ax=ax)\n",
    "fig.autofmt_xdate(bottom=0.2, rotation=30, ha='right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More comprehensive plot showing the upper and lower bound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZ59l6dKlrFq1iq+//hpfX19Ht9vLL7/Ms88+S79+/SgsLHQETXbr169H0zQ+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\nk6PRaDQajWZMop0cjUaj0Wg0Y5L/DymTKPpLFHfgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41b6ed2208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "df.plot(kind='line', x='ds', y=['Actual_Sessions', 'Projected_Sessions'], ax=ax, style=['-','--'])\n",
    "ax.fill_between(df['ds'].values, df['Projected_Sessions_lower'], df['Projected_Sessions_upper'], alpha=0.2)\n",
    "ax.set(title='Pbpython Traffic Prediction Accuracy', xlabel='', ylabel='Sessions')\n",
    "fig.autofmt_xdate(bottom=0.2, rotation=30, ha='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:pbpython3]",
   "language": "python",
   "name": "conda-env-pbpython3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
